Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://python.arviz.org/en/latest/api/generated/arviz.InferenceData.sel.html below:

arviz.InferenceData.sel — ArviZ dev documentation

InferenceData.sel(groups=None, filter_groups=None, inplace=False, chain_prior=None, **kwargs)[source]#

Perform an xarray selection on all groups.

Loops groups to perform Dataset.sel(key=item) for every kwarg if key is a dimension of the dataset. One example could be performing a burn in cut on the InferenceData object or discarding a chain. The selection is performed on all relevant groups (like posterior, prior, sample stats) while non relevant groups like observed data are omitted. See xarray.Dataset.sel

Parameters:

groupsstr or list of str, optional

Groups where the selection is to be applied. Can either be group names or metagroup names.

filter_groups{None, “like”, “regex”}, optional, default=None

If None (default), interpret groups as the real group or metagroup names. If “like”, interpret groups as substrings of the real group or metagroup names. If “regex”, interpret groups as regular expressions on the real group or metagroup names. A la pandas.filter.

inplacebool, optional

If True, modify the InferenceData object inplace, otherwise, return the modified copy.

chain_priorbool, optional, deprecated

If False, do not select prior related groups using chain dim. Otherwise, use selection on chain if present. Default=False

kwargsdict, optional

It must be accepted by Dataset.sel().

Returns:

InferenceData

A new InferenceData object by default. When inplace==True perform selection in-place and return None

See also

xarray.Dataset.sel

Returns a new dataset with each array indexed by tick labels along the specified dimension(s).

isel

Returns a new dataset with each array indexed along the specified dimension(s).

Examples

Use sel to discard one chain of the InferenceData object. We first check the dimensions of the original object:

import arviz as az
idata = az.load_arviz_data("centered_eight")
idata

• posterior

  <xarray.Dataset> Size: 165kB
  Dimensions:  (chain: 4, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 32B 0 1 2 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      mu       (chain, draw) float64 16kB ...
      theta    (chain, draw, school) float64 128kB ...
      tau      (chain, draw) float64 16kB ...
  Attributes:
      created_at:                 2022-10-13T14:37:37.315398
      arviz_version:              0.13.0.dev0
      inference_library:          pymc
      inference_library_version:  4.2.2
      sampling_time:              7.480114936828613
      tuning_steps:               1000

  • Dimensions:
    • chain: 4
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (3)

    • mu

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • theta

      (chain, draw, school)

      float64

      ...

      [16000 values with dtype=float64]

    • tau

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (6)

    created_at :

    2022-10-13T14:37:37.315398

    arviz_version :

    0.13.0.dev0

    inference_library :

    pymc

    inference_library_version :

    4.2.2

    sampling_time :

    7.480114936828613

    tuning_steps :

    1000

  
  
• posterior_predictive

  <xarray.Dataset> Size: 133kB
  Dimensions:  (chain: 4, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 32B 0 1 2 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (chain, draw, school) float64 128kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:41.460544
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 4
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (chain, draw, school)

      float64

      ...

      [16000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:41.460544

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• log_likelihood

  <xarray.Dataset> Size: 133kB
  Dimensions:  (chain: 4, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 32B 0 1 2 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (chain, draw, school) float64 128kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:37.487399
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 4
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (chain, draw, school)

      float64

      ...

      [16000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:37.487399

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• sample_stats

  <xarray.Dataset> Size: 246kB
  Dimensions:              (chain: 4, draw: 500)
  Coordinates:
    * chain                (chain) int64 32B 0 1 2 3
    * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499
  Data variables: (12/16)
      max_energy_error     (chain, draw) float64 16kB ...
      energy_error         (chain, draw) float64 16kB ...
      lp                   (chain, draw) float64 16kB ...
      index_in_trajectory  (chain, draw) int64 16kB ...
      acceptance_rate      (chain, draw) float64 16kB ...
      diverging            (chain, draw) bool 2kB ...
      ...                   ...
      smallest_eigval      (chain, draw) float64 16kB ...
      step_size_bar        (chain, draw) float64 16kB ...
      step_size            (chain, draw) float64 16kB ...
      energy               (chain, draw) float64 16kB ...
      tree_depth           (chain, draw) int64 16kB ...
      perf_counter_diff    (chain, draw) float64 16kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:37.324929
      inference_library:          pymc
      inference_library_version:  4.2.2
      sampling_time:              7.480114936828613
      tuning_steps:               1000

  • Dimensions:
  • Coordinates: (2)

    • chain

      (chain)

      int64

      0 1 2 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • Data variables: (16)

    • max_energy_error

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • energy_error

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • lp

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • index_in_trajectory

      (chain, draw)

      int64

      ...

      [2000 values with dtype=int64]

    • acceptance_rate

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • diverging

      (chain, draw)

      bool

      ...

      [2000 values with dtype=bool]

    • process_time_diff

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • n_steps

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • perf_counter_start

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • largest_eigval

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • smallest_eigval

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • step_size_bar

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • step_size

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • energy

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

    • tree_depth

      (chain, draw)

      int64

      ...

      [2000 values with dtype=int64]

    • perf_counter_diff

      (chain, draw)

      float64

      ...

      [2000 values with dtype=float64]

  • Indexes: (2)

    • PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (6)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:37.324929

    inference_library :

    pymc

    inference_library_version :

    4.2.2

    sampling_time :

    7.480114936828613

    tuning_steps :

    1000

  
  
• prior

  <xarray.Dataset> Size: 45kB
  Dimensions:  (chain: 1, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 8B 0
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      tau      (chain, draw) float64 4kB ...
      theta    (chain, draw, school) float64 32kB ...
      mu       (chain, draw) float64 4kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.602116
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 1
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (3)

    • tau

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • theta

      (chain, draw, school)

      float64

      ...

      [4000 values with dtype=float64]

    • mu

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.602116

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• prior_predictive

  <xarray.Dataset> Size: 37kB
  Dimensions:  (chain: 1, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 8B 0
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (chain, draw, school) float64 32kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.604969
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 1
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (chain, draw, school)

      float64

      ...

      [4000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.604969

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• observed_data

  <xarray.Dataset> Size: 576B
  Dimensions:  (school: 8)
  Coordinates:
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (school) float64 64B ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.606375
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
  • Coordinates: (1)

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (school)

      float64

      ...

      [8 values with dtype=float64]

  • Indexes: (1)

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.606375

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• constant_data

  <xarray.Dataset> Size: 576B
  Dimensions:  (school: 8)
  Coordinates:
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      scores   (school) float64 64B ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.607471
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
  • Coordinates: (1)

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • scores

      (school)

      float64

      ...

      [8 values with dtype=float64]

  • Indexes: (1)

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.607471

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  

In order to remove the third chain:

idata_subset = idata.sel(chain=[0, 1, 3], groups="posterior_groups")
idata_subset

• posterior

  <xarray.Dataset> Size: 125kB
  Dimensions:  (chain: 3, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 24B 0 1 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      mu       (chain, draw) float64 12kB ...
      theta    (chain, draw, school) float64 96kB ...
      tau      (chain, draw) float64 12kB ...
  Attributes:
      created_at:                 2022-10-13T14:37:37.315398
      arviz_version:              0.13.0.dev0
      inference_library:          pymc
      inference_library_version:  4.2.2
      sampling_time:              7.480114936828613
      tuning_steps:               1000

  • Dimensions:
    • chain: 3
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (3)

    • mu

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • theta

      (chain, draw, school)

      float64

      ...

      [12000 values with dtype=float64]

    • tau

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (6)

    created_at :

    2022-10-13T14:37:37.315398

    arviz_version :

    0.13.0.dev0

    inference_library :

    pymc

    inference_library_version :

    4.2.2

    sampling_time :

    7.480114936828613

    tuning_steps :

    1000

  
  
• posterior_predictive

  <xarray.Dataset> Size: 101kB
  Dimensions:  (chain: 3, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 24B 0 1 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (chain, draw, school) float64 96kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:41.460544
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 3
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (chain, draw, school)

      float64

      ...

      [12000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:41.460544

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• log_likelihood

  <xarray.Dataset> Size: 101kB
  Dimensions:  (chain: 3, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 24B 0 1 3
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (chain, draw, school) float64 96kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:37.487399
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 3
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0 1 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (chain, draw, school)

      float64

      ...

      [12000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0, 1, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:37.487399

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• sample_stats

  <xarray.Dataset> Size: 186kB
  Dimensions:              (chain: 3, draw: 500)
  Coordinates:
    * chain                (chain) int64 24B 0 1 3
    * draw                 (draw) int64 4kB 0 1 2 3 4 5 ... 495 496 497 498 499
  Data variables: (12/16)
      max_energy_error     (chain, draw) float64 12kB ...
      energy_error         (chain, draw) float64 12kB ...
      lp                   (chain, draw) float64 12kB ...
      index_in_trajectory  (chain, draw) int64 12kB ...
      acceptance_rate      (chain, draw) float64 12kB ...
      diverging            (chain, draw) bool 2kB ...
      ...                   ...
      smallest_eigval      (chain, draw) float64 12kB ...
      step_size_bar        (chain, draw) float64 12kB ...
      step_size            (chain, draw) float64 12kB ...
      energy               (chain, draw) float64 12kB ...
      tree_depth           (chain, draw) int64 12kB ...
      perf_counter_diff    (chain, draw) float64 12kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:37.324929
      inference_library:          pymc
      inference_library_version:  4.2.2
      sampling_time:              7.480114936828613
      tuning_steps:               1000

  • Dimensions:
  • Coordinates: (2)

    • chain

      (chain)

      int64

      0 1 3

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

  • Data variables: (16)

    • max_energy_error

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • energy_error

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • lp

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • index_in_trajectory

      (chain, draw)

      int64

      ...

      [1500 values with dtype=int64]

    • acceptance_rate

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • diverging

      (chain, draw)

      bool

      ...

      [1500 values with dtype=bool]

    • process_time_diff

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • n_steps

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • perf_counter_start

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • largest_eigval

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • smallest_eigval

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • step_size_bar

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • step_size

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • energy

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

    • tree_depth

      (chain, draw)

      int64

      ...

      [1500 values with dtype=int64]

    • perf_counter_diff

      (chain, draw)

      float64

      ...

      [1500 values with dtype=float64]

  • Indexes: (2)

    • PandasIndex

      PandasIndex(Index([0, 1, 3], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (6)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:37.324929

    inference_library :

    pymc

    inference_library_version :

    4.2.2

    sampling_time :

    7.480114936828613

    tuning_steps :

    1000

  
  
• prior

  <xarray.Dataset> Size: 45kB
  Dimensions:  (chain: 1, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 8B 0
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      tau      (chain, draw) float64 4kB ...
      theta    (chain, draw, school) float64 32kB ...
      mu       (chain, draw) float64 4kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.602116
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 1
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (3)

    • tau

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

    • theta

      (chain, draw, school)

      float64

      ...

      [4000 values with dtype=float64]

    • mu

      (chain, draw)

      float64

      ...

      [500 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.602116

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• prior_predictive

  <xarray.Dataset> Size: 37kB
  Dimensions:  (chain: 1, draw: 500, school: 8)
  Coordinates:
    * chain    (chain) int64 8B 0
    * draw     (draw) int64 4kB 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (chain, draw, school) float64 32kB ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.604969
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
    • chain: 1
    • draw: 500
    • school: 8
  • Coordinates: (3)

    • chain

      (chain)

      int64

      0

    • draw

      (draw)

      int64

      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499], shape=(500,))

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (chain, draw, school)

      float64

      ...

      [4000 values with dtype=float64]

  • Indexes: (3)

    • PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.604969

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• observed_data

  <xarray.Dataset> Size: 576B
  Dimensions:  (school: 8)
  Coordinates:
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      obs      (school) float64 64B ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.606375
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
  • Coordinates: (1)

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • obs

      (school)

      float64

      ...

      [8 values with dtype=float64]

  • Indexes: (1)

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.606375

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  
• constant_data

  <xarray.Dataset> Size: 576B
  Dimensions:  (school: 8)
  Coordinates:
    * school   (school) <U16 512B 'Choate' 'Deerfield' ... 'Mt. Hermon'
  Data variables:
      scores   (school) float64 64B ...
  Attributes:
      arviz_version:              0.13.0.dev0
      created_at:                 2022-10-13T14:37:26.607471
      inference_library:          pymc
      inference_library_version:  4.2.2

  • Dimensions:
  • Coordinates: (1)

    • school

      (school)

      <U16

      'Choate' ... 'Mt. Hermon'

      array(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', "St. Paul's", 'Mt. Hermon'], dtype='<U16')

  • Data variables: (1)

    • scores

      (school)

      float64

      ...

      [8 values with dtype=float64]

  • Indexes: (1)

    • PandasIndex

      PandasIndex(Index(['Choate', 'Deerfield', 'Phillips Andover', 'Phillips Exeter',
             'Hotchkiss', 'Lawrenceville', 'St. Paul's', 'Mt. Hermon'],
            dtype='object', name='school'))

  • Attributes: (4)

    arviz_version :

    0.13.0.dev0

    created_at :

    2022-10-13T14:37:26.607471

    inference_library :

    pymc

    inference_library_version :

    4.2.2

  
  

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4